Adolescent sexual and reproductive health and rights: generating evidence and strengthening country capacity
Bibliographic record
Abstract
Abstract Knowledge generation and use of evidence on Adolescent SRHR and related gender issues, (such as gender inequalities and female empowerment) is weak and is not being harnessed effectively. Country institutional capacity is often inadequate for effective analysis and communication of ASRHR data, and data on the youngest adolescent age-cohorts are lacking. Available ASRHR and related gender data through surveys are poorly harnessed by policy makers, program staff and project managers. As such, available ASRHR data are often ignored due to low confidence in research quality across data collection methods. This innovation has developed and tested improved methods of measurement, monitoring, and knowledge translation in ASRHR and gender across multiple sub-Saharan countries. It has further created and piloted methods and tools to better analyze, communicate, and translate into action ASRHR-relevant data, with a focus on better integration of national survey data (including subnational analyses, district data) to support and inform: (i) government policies and programs and local projects led by Canadian and other NGOs; and (ii) contextualized local programmatic monitoring, learning and evaluation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.389 | 0.371 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.007 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.017 | 0.018 |
| Open science | 0.004 | 0.032 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".